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Feature Selection for Banking Dataset

This project aims to utilize Genetic Algorithms (GA) to perform feature selection for a bank dataset to enhance the efficiency of predictive models.

Prerequisites

For this analysis, you need the following Python libraries:

  • pandas
  • numpy
  • sklearn
  • lightgbm
  • genetic_selection
  • matplotlib
  • itertools
  • DataTable
  • warnings

Data Preprocessing

The bank dataset bank-full.csv is loaded and preprocessed. This involves:

  • Reading the data into a pandas DataFrame.
  • Factorizing categorical data to convert them into numeric form.
  • Splitting the data into training and testing datasets.

Decision Tree

A decision tree classifier is utilized as a baseline model for comparison.

Genetic Algorithm (GA) for Feature Selection

A Genetic Algorithm approach is utilized to perform feature selection:

  • Multiple configurations of the GA parameters such as population size, crossover rate, and mutation rate are tested.
  • The best features are selected based on the highest accuracy achieved using cross-validation.
  • The results for each configuration are stored, and the best set of features is identified.

Results Visualization

Various plots are generated:

  • A plot to visualize the best fitness value of the GA across generations for different configurations.
  • Error plots to understand the standard error for various configurations of the GA.

Output

The results are stored in a CSV file named GA_report.csv.

Acknowledgment